CN110543941A - Air humidity prediction system based on CPSO-BP neural network in greenhouse strawberries - Google Patents

Air humidity prediction system based on CPSO-BP neural network in greenhouse strawberries Download PDF

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CN110543941A
CN110543941A CN201910837237.8A CN201910837237A CN110543941A CN 110543941 A CN110543941 A CN 110543941A CN 201910837237 A CN201910837237 A CN 201910837237A CN 110543941 A CN110543941 A CN 110543941A
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air humidity
neural network
humidity
cpso
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刘勇
郭丽丽
王亮
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Heilongjiang University
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Abstract

The invention discloses an air humidity prediction system in greenhouse strawberries based on a CPSO-BP neural network, which comprises a reset circuit, a first control circuit and a second control circuit, wherein the reset circuit adopts an external RST pin reset mode; a clock circuit having a control chip for recording the completion time and attached with a 31-byte static RAM; the communication circuit consists of an electric part, a charge pump circuit and a data conversion channel; the storage circuit is provided with a data storage end for storing air humidity detected at different time and is packaged by adopting a DIP8 pin; the power supply circuit is provided with a transformer and a bridge rectifier circuit, wherein the input end of the transformer is connected with a power plug through a safety switch, and the output end of the transformer is connected with the bridge rectifier circuit consisting of 4 diodes; the humidity detection circuit is provided with a detection end for detecting air humidity and a data acquisition end electrically connected with the detection end; the system can effectively improve the accuracy of the BP neural network algorithm and more effectively control the air humidity in the greenhouse strawberries.

Description

Air humidity prediction system based on CPSO-BP neural network in greenhouse strawberries
Technical Field
the invention belongs to the field of intelligent agriculture, and particularly relates to an air humidity prediction system in greenhouse strawberries based on a CPSO-BP neural network.
background
in the process of growing crops, the crops have different requirements on air humidity in different growth periods, the humidity has serious influence on the growth of the crops, the yield of the crops and the growth humidity of the crops have a close relationship, and the humidity requirement on the growth of the crops is high particularly for strawberries; at present, a circuit board is controlled through a BP neural network algorithm so as to realize the control of humidity;
The traditional BP neural network generally refers to a multi-layer feedforward neural network trained by a Back Propagation (BP) algorithm, the nonlinear mapping capability of the traditional BP neural network is weak, and the traditional BP neural network has low detection precision and cannot accurately detect humidity.
disclosure of Invention
in some optional embodiments, to solve the above technical problem, the following technical solutions are adopted in the present invention: an air humidity prediction system in greenhouse strawberries based on a CPSO-BP neural network, comprising:
The reset circuit adopts an external RST pin reset mode;
A clock circuit having a control chip for recording the completion time and attached with a 31-byte static RAM;
the communication circuit consists of an electric part, a charge pump circuit and a data conversion channel;
the storage circuit is provided with a data storage end for storing air humidity detected at different time and is packaged by adopting a DIP8 pin;
the power supply circuit is provided with a transformer and a bridge rectifier circuit, wherein the input end of the transformer is connected with a power plug through a safety switch, and the output end of the transformer is connected with the bridge rectifier circuit consisting of 4 diodes;
The humidity detection circuit is provided with a detection end for detecting air humidity and a data acquisition end electrically connected with the detection end;
The key circuit is provided with 4 keys, and comprises a parameter setting key, a starting humidity acquisition key, an plus 1 key and a minus 1 key;
the display circuit is provided with a display end for displaying the detected humidity and is provided with a controller;
The A/D (analog/digital) conversion circuit is provided with a conversion chip with a conversion analog circuit and a digital circuit, and the conversion chip is externally connected with an analog-digital conversion end;
the alarm circuit is provided with a buzzer, the buzzer is an active buzzer, and an NPN tube is externally connected with the buzzer;
one input end of the buzzer is connected with a power supply, the other end of the buzzer is connected with a collector of an NPN tube Q1, an emitter of the NPN tube Q1 is grounded, and meanwhile, a 4.7uF electrolytic capacitor is connected to a base and the emitter.
the memory circuit has self-timed erase and write cycles and up to 32 byte page write buffers, 2 ms typical write cycle time page writes, up to 8 device cascades.
The key circuit is connected with an I/O port of the singlechip through a man-in-the-wire;
the conversion chip realizes A/D conversion according to one of 8 analog input signals selected by the signal after address latch decoding
the method has the advantages that the BP nerves are utilized to detect strawberries with large humidity value deviation for many times, the average value of the strawberries is calculated, the error between the measured value of the system and the actual display value is 3 percent, and the relative error is 7 percent, so that the standard requirement of the system is met, the requirements on reliability and accuracy are met, and good help is provided for solving the problem that the detection of the existing strawberry humidity technology is not easy to control;
the system also comprises an upper computer and a lower computer;
the upper computer is the core for controlling the whole detection system, and detection and control are implemented by adopting configuration software, so that the running state of a key part can be displayed, and the acquired signals can be analyzed, judged and fed back for optimization;
The lower computer adopts programmable control logic as a control chip, collects the signals of the humidity sensor and processes the collected data;
The crystal oscillator circuit provides the required oscillation frequency for the humidity sensor, the power supply circuit can realize the conversion from 220V alternating current to 5V direct current, and the information acquired by the humidity sensor is carried out through an ADC0809 chip;
the data are sent to a microprocessor unit after analog-to-digital conversion, so that the functions of displaying humidity and alarming for overrun conditions are realized, and meanwhile, the data can be effectively stored internally and accompanied with a real-time communication function;
The strawberry is sampled, the humidity sensor is used for collecting data, and detected analog signals are transmitted to the microprocessor unit through the A/D conversion circuit. Under the conditions that the power supply circuit provides stable working voltage, the crystal oscillator circuit provides stable oscillation frequency and the reset circuit can realize automatic reset, the microprocessor unit respectively realizes alarm of humidity overrun, display of humidity value, display of time, internal storage of data and communication with an external circuit through the program control alarm circuit, the display circuit, the clock circuit, the storage circuit and the communication circuit;
The effect of man-machine interaction is achieved by editing the program through the key circuit, when the reset button is pressed, the chip pin RST can send a received low-level signal to the CPU, and therefore the minimum system is reset reliably. The STC12C5A60S2 single chip microcomputer is provided with a MAX810 special reset circuit, and if the software is allowed in a downloader during software programming, the reset can be released only by delaying 200mS after the system is powered on and reset;
the clock chip is attached with 31 bytes of static RAM, so that the data and the time when the data appears can be recorded simultaneously. Meanwhile, the synchronous communication of the chip and the CPU uses an SPI three-wire interface, the input end of a transformer can send multi-byte RAM data and clock signals at one time in a burst mode and is connected with a power plug through a safety connector, the output end of the transformer is connected with a bridge rectifier circuit consisting of 4 diodes, and a 330uF electrolytic capacitor is connected to the output end of the transformer because the fluctuation of direct-current voltage output by rectification is high. The 9V voltage at the output end of the transformer is rectified by a bridge type rectifier and filtered by a capacitor, and more than 11V is approximately generated at two ends of the capacitor C4;
The voltage circuit module is provided with 4 keys which comprise a parameter setting key, a starting humidity acquisition key, an plus 1 key and a minus 1 key. The single chip microcomputer is combined with the key circuits in an independent connection mode, namely each key is independently connected with one I/O port of the single chip microcomputer through a man-wire, so that the aim that each key circuit is not interfered with each other is fulfilled;
the single chip microcomputer monitors the key circuit in an interrupt inquiry mode by adopting an ADC0809 chip, and can realize A/D conversion by selecting one of 8 analog input signals according to a signal after address latching and decoding, wherein the input end of the A/D conversion circuit is connected with the humidity acquisition circuit, and the output end of the A/D conversion circuit is connected with the single chip microcomputer. The humidity acquisition circuit adopts an HIH3610 humidity sensor with low cost, high sensitivity, low power consumption and strong stability. The measurement of humidity for the HIH3610 humidity sensing is not constrained by atmospheric pressure.
On the other hand, the system provides a CPSO-BP neural algorithm, the BP neural network is trained by an error inverse propagation algorithm and has stronger nonlinear mapping capability, and the BP neural network which is improved by embedding a guide module in a hidden layer of the CPSO-BP neural network not only has certain self-adaption and learning capabilities, but also can realize a complex nonlinear mapping function;
the characteristic vector of the original dissolved gas content characteristic information of the transformer, which is output after the characteristic mapping of the multilayer network, not only fuses various gas content information, but also expands the original characteristic attribute dimension. Meanwhile, the guiding module is used for increasing the value of extracting the feature vector of the better layer and reducing the value of extracting the feature vector of the poorer layer, so that the fused and expanded better feature vector has more obvious feature difference;
in addition, when the SVM is used for diagnosing and classifying data (feature vectors) with obvious feature differences, the diagnosis and classification accuracy of the SVM can be better improved. Therefore, the diagnosis performance of the SVM transformer fault diagnosis method for improving the BP neural network is better than that of the BP neural network and the SVM diagnosis method.
Advantageous effects
according to the method, in the process of cultivating the greenhouse strawberries, the growth process of the strawberries is inevitably influenced by the air humidity, and the growth of the greenhouse strawberries is very sensitive to the air humidity, so that the prediction of the air humidity in the greenhouse is an important link for cultivating the greenhouse strawberries, but the traditional algorithm for predicting the air humidity, such as a BP (back propagation) neural network, has the problems of local minimization, low convergence rate and the like, and seriously influences the precision of the prediction algorithm, however, the precision of the BP neural network algorithm can be effectively improved through the CPSO-BP neural network, and the air humidity in the greenhouse strawberries can be more effectively controlled.
Drawings
in order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the description of the embodiments will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art that other drawings can be obtained according to the drawings without creative efforts.
FIG. 1 is a block diagram of the overall design of the present invention;
FIG. 2 is a key flow diagram of the present invention;
FIG. 3 is a flowchart of the BP neural test humidity of the present invention;
Detailed Description
the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
referring to fig. 1, 2 and 3, the strawberries with large strawberry humidity value deviation are detected for multiple times by using BP nerves, an average value is calculated, the error between a measured value and an actual display value of a system is 3 percent, and the relative error is 7 percent, so that the standard requirement of the system is met, the requirements of reliability and accuracy are met, and good help is provided for solving the problem that the detection of the existing strawberry humidity technology is not easy to control;
The system also comprises an upper computer and a lower computer;
The upper computer is the core for controlling the whole detection system, and detection and control are implemented by adopting configuration software, so that the running state of a key part can be displayed, and the acquired signals can be analyzed, judged and fed back for optimization;
The lower computer adopts programmable control logic as a control chip, collects the signals of the humidity sensor and processes the collected data;
The crystal oscillator circuit provides the required oscillation frequency for the humidity sensor, the power supply circuit can realize the conversion from 220V alternating current to 5V direct current, and the information acquired by the humidity sensor is carried out through an ADC0809 chip;
the data are sent to a microprocessor unit after analog-to-digital conversion, so that the functions of displaying humidity and alarming for overrun conditions are realized, and meanwhile, the data can be effectively stored internally and accompanied with a real-time communication function;
the strawberry is sampled, the humidity sensor is used for collecting data, and detected analog signals are transmitted to the microprocessor unit through the A/D conversion circuit. Under the conditions that the power supply circuit provides stable working voltage, the crystal oscillator circuit provides stable oscillation frequency and the reset circuit can realize automatic reset, the microprocessor unit respectively realizes alarm of humidity overrun, display of humidity value, display of time, internal storage of data and communication with an external circuit through the program control alarm circuit, the display circuit, the clock circuit, the storage circuit and the communication circuit;
the effect of man-machine interaction is achieved by editing the program through the key circuit, when the reset button is pressed, the chip pin RST can send a received low-level signal to the CPU, and therefore the minimum system is reset reliably. The STC12C5A60S2 single chip microcomputer is provided with a MAX810 special reset circuit, and if the software is allowed in a downloader during software programming, the reset can be released only by delaying 200mS after the system is powered on and reset;
the clock chip is attached with 31 bytes of static RAM, so that the data and the time when the data appears can be recorded simultaneously. Meanwhile, the synchronous communication of the chip and the CPU uses an SPI three-wire interface, the input end of a transformer can send multi-byte RAM data and clock signals at one time in a burst mode and is connected with a power plug through a safety connector, the output end of the transformer is connected with a bridge rectifier circuit consisting of 4 diodes, and a 330uF electrolytic capacitor is connected to the output end of the transformer because the fluctuation of direct-current voltage output by rectification is high. The 9V voltage at the output end of the transformer is rectified by a bridge type rectifier and filtered by a capacitor, and more than 11V is approximately generated at two ends of the capacitor C4;
the voltage circuit module is provided with 4 keys which comprise a parameter setting key, a starting humidity acquisition key, an adding 1 key and a subtracting 1 key, the single chip microcomputer is combined with the key circuits in an independent connection mode, namely, each key is independently connected with an I/O port of the single chip microcomputer through a man-wire, and the purpose that each key circuit is not interfered with each other is achieved;
the single chip microcomputer monitors the key circuit in an interrupt inquiry mode by adopting an ADC0809 chip, and can realize A/D conversion by selecting one of 8 analog input signals according to a signal after address latching and decoding, wherein the input end of the A/D conversion circuit is connected with the humidity acquisition circuit, and the output end of the A/D conversion circuit is connected with the single chip microcomputer. The humidity acquisition circuit adopts an HIH3610 humidity sensor with low cost, high sensitivity, low power consumption and strong stability. The measurement of humidity for the HIH3610 humidity sensor is not constrained by atmospheric pressure;
and establishing a fuzzy control table according to the control requirement, measuring data by the sensor, enabling the measured value to correspond to the response fuzzy quantity in the fuzzy control table, analyzing and calculating the response fuzzy quantity by the controller, converting the result into an accurate quantity, and then controlling the actuator to act. The application of the fuzzy control technology overcomes the defect of insufficient design of the traditional controller and brings feasibility for realizing effective control. Meanwhile, the fuzzy neural network has the characteristics of vivid image, easy construction, low cost, strong robustness and anti-interference capability on system parameter change and the like, wherein the fuzzy neural network can gradually achieve nonlinear mapping through continuous identification and control, can jump out the constraint of the nonlinear mapping, and has stronger advantages compared with the traditional control method.
the realization of the whole control system needs the cooperation of an upper computer and a lower computer, functions such as state display, user management, data storage, trend curve generation and the like need to be designed by the upper computer, and functions such as input and output of control signals, acquisition of monitoring data and the like need to be completed by the lower computer. The process of the system design comprises five steps of analyzing system requirements, designing system hardware, designing a PLC program, designing an upper computer and debugging the system. The system hardware design comprises acquisition interface distribution, PLC module combination and PLC I/O distribution.
in the description herein, references to the description of "one embodiment," "an example," "a specific example" or the like are intended to mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the invention. In this specification, the schematic representations of the terms used above do not necessarily refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
the preferred embodiments of the invention disclosed above are intended to be illustrative only. The preferred embodiments are not intended to be exhaustive or to limit the invention to the precise embodiments disclosed. Obviously, many modifications and variations are possible in light of the above teaching. The embodiments were chosen and described in order to best explain the principles of the invention and the practical application, to thereby enable others skilled in the art to best utilize the invention. The invention is limited only by the claims and their full scope and equivalents.

Claims (5)

1. a system for predicting air humidity in greenhouse strawberries based on a CPSO-BP neural network is characterized by comprising,
the reset circuit adopts an external RST pin reset mode;
a clock circuit having a control chip for recording the completion time and attached with a 31-byte static RAM;
The communication circuit consists of an electric part, a charge pump circuit and a data conversion channel;
the storage circuit is provided with a data storage end for storing air humidity detected at different time and is packaged by adopting a DIP8 pin;
the power supply circuit is provided with a transformer and a bridge rectifier circuit, wherein the input end of the transformer is connected with a power plug through a safety switch, and the output end of the transformer is connected with the bridge rectifier circuit consisting of 4 diodes;
the humidity detection circuit is provided with a detection end for detecting air humidity and a data acquisition end electrically connected with the detection end;
the key circuit is provided with 4 keys, and comprises a parameter setting key, a starting humidity acquisition key, an plus 1 key and a minus 1 key;
the display circuit is provided with a display end for displaying the detected humidity and is provided with a controller;
The A/D (analog/digital) conversion circuit is provided with a conversion chip with a conversion analog circuit and a digital circuit, and the conversion chip is externally connected with an analog-digital conversion end;
and the alarm circuit is provided with a buzzer, the buzzer is an active buzzer, and the buzzer is externally connected with an NPN tube.
2. The system for predicting the air humidity in the strawberries in the greenhouse based on the CPSO-BP neural network as claimed in claim 1, wherein: one input end of the buzzer is connected with a power supply, the other end of the buzzer is connected with a collector of an NPN tube Q1, an emitter of the NPN tube Q1 is grounded, and meanwhile, a 4.7uF electrolytic capacitor is connected to a base and the emitter.
3. The system for predicting the air humidity in the strawberries in the greenhouse based on the CPSO-BP neural network as claimed in claim 1, wherein: the memory circuit has self-timed erase and write cycles and up to 32 byte page write buffers, 2 ms typical write cycle time page writes, up to 8 device cascades.
4. the system for predicting the air humidity in the strawberries in the greenhouse based on the CPSO-BP neural network as claimed in claim 1, wherein: the key circuit is connected with an I/O port of the singlechip through a man-in-the-wire.
5. The system for predicting the air humidity in the strawberries in the greenhouse based on the CPSO-BP neural network as claimed in claim 1, wherein: the conversion chip is used for realizing A/D conversion according to one of the 8 analog input signals selected by the signal gating after address latching and decoding.
CN201910837237.8A 2019-09-05 2019-09-05 Air humidity prediction system based on CPSO-BP neural network in greenhouse strawberries Pending CN110543941A (en)

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CN201910837237.8A CN110543941A (en) 2019-09-05 2019-09-05 Air humidity prediction system based on CPSO-BP neural network in greenhouse strawberries

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CN201910837237.8A CN110543941A (en) 2019-09-05 2019-09-05 Air humidity prediction system based on CPSO-BP neural network in greenhouse strawberries

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